R - How to operate on different columns for each row based on an extra-column containing the names o-CodePudding

I am new to R. I would like to calculate the mean for each row of a dataframe, but using different subset of columns for each row. I have two extra-columns providing me the names of the column that represent the "start" and the "end" that I should use to calculate each mean, respectively.

Let's take this example

dframe <- data.frame(a=c("2","3","4", "2"), b=c("1","3","6", "2"), c=c("4","5","6", "3"), d=c("4","2","8", "5"), e=c("a", "c", "a", "b"), f=c("c", "d", "d", "c"))
dframe

Which provides the following dataframe:

  a b c d e f
1 2 1 4 4 a c
2 3 3 5 2 c d
3 4 6 6 8 a d
4 2 2 3 5 b c

The columns e and f represent the first and last column I use to calculate the mean for each row. For example, on line 1, the mean would be calculated including column a, b, c ((2 1 4)/3 -> 2.3) So I would like to obtain the following output:

  a b c d e f mean
1 2 1 4 4 a c  2.3
2 3 3 5 2 c d  3.5
3 4 6 6 8 a d    6
4 2 2 3 5 b c  2.5

I learnt how to create the indices, and I want then to use RowMeans, but I cannot find the correct arguments.

dframe %>%
  mutate(e_indice = match(e, colnames(dframe)))%>%
  mutate(f_indice = match(f, colnames(dframe)))%>%
  mutate(mean = RowMeans(????, na.rm = TRUE))

Thanks a lot for your help

CodePudding user response：

One dplyr option could be:

dframe %>%
    rowwise() %>%
    mutate(mean = rowMeans(cur_data()[match(e, names(.)):match(f, names(.))]))

      a     b     c     d e     f      mean
  <dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl>
1     2     1     4     4 a     c      2.33
2     3     3     5     2 c     d      3.5 
3     4     6     6     8 a     d      6   
4     2     2     3     5 b     c      2.5

CodePudding user response：

I would define a helper function that lets you slice the indices you want from a matrix.

rowSlice <- function(x, start, stop) {
  replace(x, col(x) < start | col(x) > stop, NA)
}

rowSlice(matrix(1, 4, 4), c(1, 3, 1, 2), c(3, 4, 4, 3))
#>      [,1] [,2] [,3] [,4]
#> [1,]    1    1    1   NA
#> [2,]   NA   NA    1    1
#> [3,]    1    1    1    1
#> [4,]   NA    1    1   NA

Then use across() to select the relvant columns, slice them, and take the rowMeans().

library(dplyr)

dframe <- data.frame(
  a = c(2, 3, 4, 2),
  b = c(1, 3, 6, 2),
  c = c(4, 5, 6, 3),
  d = c(4, 2, 8, 5),
  e = c("a", "c", "a", "b"),
  f = c("c", "d", "d", "c")
)

dframe %>%
  mutate(ei = match(e, colnames(dframe))) %>%
  mutate(fi = match(f, colnames(dframe))) %>% 
  mutate(
    mean = across(a:d) %>%
      rowSlice(ei, fi) %>%
      rowMeans(na.rm = TRUE)
  )
#>   a b c d e f ei fi     mean
#> 1 2 1 4 4 a c  1  3 2.333333
#> 2 3 3 5 2 c d  3  4 3.500000
#> 3 4 6 6 8 a d  1  4 6.000000
#> 4 2 2 3 5 b c  2  3 2.500000

CodePudding user response：

A base R solution. First, set columns to numeric. Then create a list of the columns on which to apply the mean. Then apply mean on selected columns.

s <- mapply(seq, match(dframe$e, colnames(dframe)), match(dframe$f, colnames(dframe)))
dframe$mean <- lapply(seq(nrow(dframe)), function(x) rowMeans(dframe[x, s[[x]]]))

  a b c d e f     mean
1 2 1 4 4 a c 2.333333
2 3 3 5 2 c d      3.5
3 4 6 6 8 a d        6
4 2 2 3 5 b c      2.5

CodePudding user response：

A base R approach using apply

dframe$mean <- apply(dframe, 1, function(x) 
  mean(as.numeric(x[which(names(x) == x["e"]) : which(names(x) == x["f"])])))

dframe
  a b c d e f     mean
1 2 1 4 4 a c 2.333333
2 3 3 5 2 c d 3.500000
3 4 6 6 8 a d 6.000000
4 2 2 3 5 b c 2.500000